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- W4229017647 abstract "Recent studies indicate that detecting radiographic patterns on CT scans can yield high sensitivity and specificity for Covid-19 localization. In this paper, we investigate the appropriateness of deep learning models transferability, for semantic segmentation of pneumonia-infected areas in CT images. Transfer learning allows for the fast initialization/reutilization of detection models, given that large volumes of training data are not available. Our work explores the efficacy of using pre-trained U-Net architectures, on a specific CT data set, for identifying Covid-19 side-effects over images from different datasets. Experimental results indicate improvement in the segmentation accuracy of identifying Covid-19 infected regions." @default.
- W4229017647 created "2022-05-08" @default.
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- W4229017647 date "2022-05-04" @default.
- W4229017647 modified "2023-09-26" @default.
- W4229017647 title "Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models" @default.
- W4229017647 doi "https://doi.org/10.48550/arxiv.2205.02152" @default.
- W4229017647 hasPublicationYear "2022" @default.
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